Selling green first or not? A Bayesian analysis with service levels and environmental impact considerations in the Big Data Era

Bin Shen, Tsan-Ming Choi and Hau-Ling Chan

Technological Forecasting and Social Change, 2019, vol. 144, issue C, 412-420

Abstract: Nowadays, retailers sell both green and non-green products to consumers. In many real world retailing businesses, owing to shelf-space limits and to avoid cannibalization between products, retailers only sell either the green or the non-green product but not both at the same time. Then, an important question arises: Should the retailer sell the green product first or not? This question is critical in the big data era because the retailer can use advanced technologies to collect a massive amount of demand data of the product sold first and then update the demand forecast for the forthcoming product, which improves its retail business performance and services. In this paper, by constructing a Bayesian information inventory updating model, we identify the analytical conditions for the retailer to decide the optimal selling sequence. Moreover, we uncover that when the green product's service level is lower than the non-green one's, selling the green product first surely produces a lower environmental cost. However, when the green product's service level is higher than the non-green one's, selling the green product first does not always produce a lower environmental cost. Furthermore, we investigate the impact of big data on the profit improvement and the environmental cost improvement, and its relationship with the optimal selling sequence.

Keywords: Service level; Retail marketing; Environmental impact; Bayesian information updating; Use of information; Green product; Big data (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:144:y:2019:i:c:p:412-420

DOI: 10.1016/j.techfore.2017.09.003

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